Friction stir spot welding (FSSW) is a solid-state joining process and a rapidly growing dissimilar material welding technology for joining metallic alloys in the automotive industry. Welding tool shape and process conditions must be appropriately controlled to obtain high bonding characteristics. In this study, FSSW is performed on dissimilar materials AA5052-H32 aluminum alloy sheet and SPRC440 steel sheet, and the influence of the shape of joining tool and tool insertion depth during joining is investigated. A new intermetallic compound is produced at the aluminum and steel sheets joint. When the insertion depth of the tool is insufficient, the intermetallic compound between the two sheets did not form uniformly. As the insertion depth increased, the intermetallic compound layer become uniform and continuous. The joint specimen shows higher values of tensile shear load as the diameter and insertion depth of the tool increase. This shows that the uniform formation of the intermetallic compound strengthens the bonding force between the joining specimens and increases the tensile shear load.

Spot welding is a representative process in automotive welding and the application of intelligent systems is accelerating. In particular, in the case of welding electrode management, the timing of electrode wear and dressing was determined by continuous spot welding evaluation, however there is concerned that errors in welding equipment or processes may work in a complex manner. In this study, a dynamic resistance waveform sensing and image measurement system that greatly affects the nugget formation, which is important to the quality of spot welding, was fabricated and used. Based on the experimental data of the galvanized steel sheet, an electrode life prediction algorithm for electrode wear was derived through CNN(Convolutional Neural Network) model of machine learning training.

To conduct a nuclear fuel irradiation test, the inside of the nuclear fuel rod must be assembled along with the test fuel, several different parts, and sensors, and then filled with high-pressure and high-purityhelium gas. Therefore, it is necessary to develop helium gas filling techniques that can achieve exact TIG (Tungsten Inert Gas) spot welding at a pin-hole of the nuclear fuel rod to fill helium gas into the nuclear fuel test rod. However, previous apparatuses do not have repeatability for TIG spot welding as they lack an electrode position control jig to exactly fix a TIG electrode in a high-pressure chamber, and they consume a large amount of helium gas. Therefore, a TIG spot welding apparatus was developed to easily and accurately conduct TIG spot welding and significantly reduce the gas consumption. In addition, the optimum welding conditions of this welding apparatus were established through various weld tests.

To estimate weld quality of the resistance spot-welding, the acoustic emission features are investigated from the total acoustic emission signal at the single-spot weld. Typically, the resistance spot welding process consists of several stages: set-down of the electrodes, squeeze, current flow, forging, hold time, and lift-off. Various types of acoustic emission response corresponding to each stage can be separately analyzed by using back-propagation neural network classifier and wavelet transform technique. The presented machine learning results provide a validation for using back-propagation neural network and wavelet transform technique as a valuable insights into the resistance spot-welding process. Especially, a wavelet transform technique is demonstrated and the plots are very powerful in the recognition of the acoustic emission features

This study analyzes the resistance spot weldability of DP60 steels. To analyze the resistance spot weldability of DP60 steels, tensile strength test and macro-section test were conducted for the resistance spot welds. Acceptable welding conditions were determined as a function of the resistance spot welding process parameters such as electrode force, welding time, and welding current. The lower limit of the welding lobe was the minimum shear tension strength for 590MPa-grade steel while the upper limit was determined whether or not expulsion was detected.

The spot weldability of dissimilar metal joints between stainless steels (AISI316) and interstitial free (IF) steels were investigated. This study was aimed to determine the spot welding parameters for a dissimilar metal joint and to evaluate the dissimilar metal joint's weldability, including its welding nugget shape, tensileshear strength, hardness, and microstructure. The fracture surface was investigated by using a Scanning Electron Microscopy (SEM). The experimental results showed that the shape of nugget was asymmetric, in which the fusion zone of the STS316 sheet was larger due to the higher bulk-resistance. The microstructure of the fusion zone was fully martensite. In order to evaluate the microstructure further, dilution of stainless steels were calculated and imposed onto the Schaeffler diagram. The predicted microstructure from the Schaeffler diagram was martensite. In order to confirm the predicted microstructure, XRD measurements were carried out. The results showed that that initial weld nugget was composed of austenite and martensite.